With the rapid development of global offshore wind power, the demand for offshore wind power operation and maintenance is also increasing. Wisdomization of offshore wind farms is a practical need to improve the operation level and benefit of offshore wind farms. This paper first introduces the current development situation and characteristics of global offshore wind power, and expounds the current situation and main challenges of offshore wind power operation and maintenance market. Therefore, our paper discusses the innovation of offshore wind power operation and maintenance from the aspects of operation and maintenance management of offshore wind power, monitoring and analysis technology of units, far-reaching wind field monitoring and operation and maintenance risks. Then, combined with information technology and lean management concept, a smart operation and maintenance management platform for wind farms in far-reaching sea areas is built to explore centralized and intelligent operation and maintenance management mode, improve operation and maintenance efficiency of wind farms in far-reaching sea areas, and minimize operation and maintenance costs. Finally, through the research on the characteristics of 5G technology, combined with the practical experience of operation and maintenance, and in view of the characteristics of offshore wind farms, we analyze and propose several typical application scenarios of 5G technology in the intelligent operation and maintenance of offshore wind farms, which provides a new solution for the efficient operation and maintenance of offshore wind farms.

Automatic target detection of remote sensing images (RSI) plays an important role in military reconnaissance, disaster monitoring, and target rescue. The core task of remote sensing target detection is to judge the target categories and complete precise location. However, the existing target detection algorithms have limited accuracy and weak generalization capability for remote sensing images with complex backgrounds. To achieve accurate detection of different categories targets in remote sensing images, this study presents a novel feature enhancement single shot multibox detector (FESSD) algorithm for remote sensing target detection. The FESSD introduces feature enhancement module and attention mechanism into the convolution neural networks (CNN) model, which can effectively enhance the feature extraction ability and nonlinear relationship between different convolution features. Specifically, the feature enhancement module is used to extract the multi-scale feature information, and enhance the model nonlinear learning ability; the self-learning attention mechanism (SAM) is used to expand the convolution kernel local receptive field, which makes the model extract more valuable features. In addition, the nonlinear relationship between different convolution features is enhanced using the feature pyramid attention mechanism (PAM). The advantage of FESSD over other state-of-the-art target detection methods is validated by experiments on the presented seven-class target detection dataset (SD-RSI) and the public DIOR dataset.

Understanding how industrial agglomeration affects agricultural green production efficiency is essential for green agricultural development. This study uses the super-efficient EBM-GML to measure and analyze the spatial and temporal evolution characteristics and core sources of dynamics of agricultural green production efficiency in China by using panel data from 30 Chinese provinces from 2006 to 2020. It also empirically investigates the relationships between industrial agglomeration, land transfer, and agricultural production efficiency. By using fixed, intermediary, and threshold effects models, the internal links between the industrial agglomeration, land transfer, and agricultural green production efficiency are examined. The findings indicate that the green production efficiency of Chinese agriculture exhibits the regional characteristics of being “high in the west and low in the east, high in the south and low in the north” in terms of space; in terms of time series, the overall trend is that of growing, with an average annual growth rate of 11.45%, and the growth primarily depends on the “single-track drive” of green technological progress. By promoting land transfer, industrial agglomeration can increase the agricultural green production efficiency and decomposition index. Land transfer has a double-threshold effect on the influence of industrial agglomeration on agricultural green production efficiency. As a result, this study suggests adhering to the idea of synergistic development, promoting agricultural green development, strengthening the development of industrial agglomerations, promoting the quality and efficiency of industry, improving land-transfer mechanisms, and placing a focus on resource efficiency improvements, as well as other policy recommendations.

Increasing green total factor productivity is the key to achieving green development in agriculture. This study measured the green total factor productivity of Jiangxi’s agriculture, and its regional and temporal evolution characteristics were examined. The fixed-effects model was then used to investigate the model’s fundamental components empirically. The study’s findings reveal the following: During the period under review, technical change was the primary element driving the rise in the green total factor productivity of agriculture. A rising “U”-shaped trend with notable regional variances characterizes the spatial and temporal evolution. The primary factors that affect changes in green total factor productivity in agriculture include the ease of transportation, the per capita disposable income of rural residents, the level of agricultural mechanization, the degree of urbanization, the level of financial support for agriculture, and the percentage of workers in secondary industries. As a result, it is suggested that, in order to substantially increase agricultural green total factor productivity, agricultural green technological support should be strengthened, more fully developed, and promoted in a multi-pronged approach.

Predatory mites are a type of biological control agent used in many countries against various vegetable pests, particularly spider mites. Despite the significant presence of predatory mites in the Tibetan plateau, there is limited research on their fitness and prey capacity in the area. Herein, four predatory mite species, including one species Neoseiulus californicus and three species from genus Amblyseius (Amblyseius swirskii, Amblyseius barkeri, and Amblyseius cucumeris) were investigated. Specifically, their fitness and prey capacity on two-spotted mites (Tetranychus urticae) at highland conditions were evaluated. Compared to the other three predatory species, A. swirskii had the highest fecundity (Mean ± SEM=11.60 ± 0.34) and the highest pre-adult survival rate (Mean ± SEM = 83.33 ± 3.33%). Since their juvenile Survival Rate (SR) was extremely low (Mean ± SEM =13.33% ± 5.77%), most A. barkeri nymphs died before emergence. Compared to the other three predatory mites, A. swirskii had the highest predation capacity on T. urticae adult mites at 15 d post-release (Mean ± SEM =14.28 ± 2.24). Collectively, these findings imply notable advantages in employing A. swirskii for controlling two-spotted mites in the Tibetan Plateau. Furthermore, based on the results, A. swirskii was the most effective, and A. barkeri was the least effective in controlling two-spotted mites in the Tibetan Plateau. These findings could inform the development of a feasible biological control method based on suitable predatory mite species to manage T. urticae in the Tibetan Plateau, and would be helpful in delaying the rise or emergence of chemical pesticide resistance in two-spotted mites.

Hot deformation at elevated temperature is essential to densify the particle reinforced Aluminum matrix composites (AMCs) and improve their performance. However, hot deformation behavior of the AMCs is sensitive to the variation of hot processing parameters. In this paper, optimal processing parameters of the dual-scale SiCp/A356 composites was determined to explore the control strategy of the microstructure. Hot compression tests were conducted at the temperature ranging from 460 to 520℃ under strain rates from 0.01 to 5 s−1. Constitutive equation and processing maps were presented to determine the hot processing parameters. Microstructure evolution of the dual-scale SiCp/A356 composites was analyzed. The strain rate of 0.62 ~ 5s-1 and deformation temperature of 495 ~ 518℃ is suitable for the hot processing. The number of DRX grains in the “safe” domains is larger and the dislocation density is lower compared to those of instability domains. DRX particles mainly occurred around SiC particles. The presence of SiC particles can promote effectively the DRX nucleation, which result in that the dynamic softening mechanism of the dual-scale SiCp/A356 composites is dominated DRX.

MultiCal is an affordable, high-precision measuring device designed for the on-site calibration of industrial robots. Its design features a long measuring rod with a spherical tip that is attached to the robot. By restricting the rod’s tip to multiple fixed points under different rod orientations, the relative positions of these points are accurately measured beforehand. A common issue with MultiCal is the gravity deformation of the long measuring rod, which introduces measurement errors into the system. This problem becomes especially serious when calibrating large robots, as the length of the measuring rod needs to be increased to enable the robot to move in a sufficient space. To address this issue, we propose two improvements in this paper. Firstly, we suggest the use of a new design of the measuring rod that is lightweight yet has high rigidity. Secondly, we propose a deformation compensation algorithm. Experimental results have shown that the new measuring rod improves calibration accuracy by 20% to 39%, while by using the deformation compensation algorithm, the accuracy increases by 6% to 16%. In the best configuration, the calibration accuracy is similar to that of a measuring arm with a laser scanner, producing an average positioning error of 0.274 mm and a maximum positioning error of 0.838 mm. The improved design is cost-affordable, robust, and has sufficient accuracy, making MultiCal a more reliable tool for industrial robot calibration.

Mn-doped CeO2 and CeO2 with the same morphology (nanofiber and nanocube) were synthesized through hydrothermal method respectively. When applied to benzene oxidation, the catalytic properties of Mn-doped CeO2 were higher than those of CeO2, which was related with the concentration of O vacancy. Compared to CeO2 with the same morphology, more oxygen vacancies were formed on the surface of Mn-doped CeO2, due to Mn ion replacing Ce ion. The lattice replacement was analyzed through XRD, Raman, electron energy loss spectroscopy and electron paramagnetic resonance technology. The formation energies of oxygen vacancy on the different exposed crystal planes [(110) and (100)] for Mn-doped CeO2 were calculated by applying the density functional theory (DFT). The data showed that the oxygen vacancy was easier to be formed on the (110) plane. The factors influencing catalytic behavior were elaborated, which indicated that surface oxygen vacancy played an important part in catalytic reaction.

Panax notoginseng, as traditional Chinese medicine, has a long history of high clinical value, such as anti-inflammatory, anti-oxidation, inhibition of platelet aggregation, regulation of blood glucose and blood pressure, inhibition of neuronal apoptosis and neuronal protection, and its main ingredients are Panax notoginseng saponins (PNS). Currently, Panax notoginseng may improve mental function, have anti-insomnia and anti-depression effects, alleviate anxiety, and decrease neural network excitation. However, the underlying effects and the mechanisms of Panax notoginseng and its containing chemical constituents (PNS) on these depression-related or anxiety-related diseases has not been completely established. This review summarized the antidepressant or anxiolytic effects and mechanisms of PNS, and analyzed network targets of antidepressant or anxiolytic actions with network pharmacology tools to provide directions and references for further pharmacological studies and new ideas for clinical treatment of nervous system diseases and drug studies and development.

Intercropping significantly improves land use efficiency and soil fertility. This study examines the impact of three cultivation systems (monoculture sugarcane, peanut-sugarcane and soybean-sugarcane intercropping) on soil properties and diazotrophs. Sugarcane rhizosphere soil was sampled from the farmers’ field. Soil properties and nifH gene abundance were analyzed by high throughput sequencing. Moreover, a total of 436,458 nifH gene sequences were obtained and classified into the 3201 unique operational taxonomic units (OTUs). Maximum unique OTUs resulted with soybean-sugarcane intercropping (<375). The dominant groups across all cultivation were Alpha-proteobacteria and Beta-proteobacteria. On the basis of microbial community structure, intercropping systems were more diverse than monoculture sugarcane. In the genus level, Bradyrhizobium, Burkholderia, Pelomonas, and Sphingomonas were predominant in the intercropping systems. Moreover, diazotrophic bacterial communities of these cultivation systems were positively correlated to the soil pH and soil enzyme protease. Moreover, low available P recovered from intercropping system showed a strong correlation with higher nutrient uptake activity of soil microbes. Based on the results, our investigation concluded that intercropping system caused a positive effect on the growth of diazotrophic bacterial communities and it might boost the soil fertility and this kind of study helps to develop an eco-friendly technology for sustainable sugarcane production.

Ischemic stroke is one of the most disabling diseases and a leading cause of death globally. Despite advances in medical care, the global burden of stroke continues to grow, as no effective treatments to limit or reverse ischemic injury to the brain are available. However, recent preclinical findings have revealed the potential role of transient receptor potential cation 6 (TRPC6) channels as endogenous protectors of neuronal tissue. Activating TRPC6 in various cerebral ischemia models has been found to prevent neuronal death, whereas blocking TRPC6 enhances sensitivity to ischemia. Evidence has shown that Ca2+ influx through TRPC6 activates cAMP response element-binding protein (CREB), an important transcription factor linked to neuronal survival. Additionally, TRPC6 activation may counter excitotoxic damage resulting from glutamate release by attenuating the activity of NMDA receptors of neurons by posttranslational means. Unresolved though, are the roles of TRPC6 channels in non-neuronal cells such as astrocytes and endothelial cells. Moreover, TRPC6 channels may have detrimental effects on the blood-brain barrier, although their exact role in neurovascular coupling requires further investigation. This review discusses evidence-based cell-specific aspects of TRPC6 in the brain to assess the potential targets for ischemic stroke management.